import pandas as pd
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt       # matplotlib.pyplot plots data
%matplotlib inline 
import seaborn as sns
import scipy.stats as stats
from sklearn.preprocessing import LabelEncoder

pd.set_option('display.max_columns', None)

data=pd.read_csv('vehicle-1.csv')

datao=data.copy()

EDA

data.head()

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class
0	95	48.0	83.0	178.0	72.0	10	162.0	42.0	20.0	159	176.0	379.0	184.0	70.0	6.0	16.0	187.0	197	van
1	91	41.0	84.0	141.0	57.0	9	149.0	45.0	19.0	143	170.0	330.0	158.0	72.0	9.0	14.0	189.0	199	van
2	104	50.0	106.0	209.0	66.0	10	207.0	32.0	23.0	158	223.0	635.0	220.0	73.0	14.0	9.0	188.0	196	car
3	93	41.0	82.0	159.0	63.0	9	144.0	46.0	19.0	143	160.0	309.0	127.0	63.0	6.0	10.0	199.0	207	van
4	85	44.0	70.0	205.0	103.0	52	149.0	45.0	19.0	144	241.0	325.0	188.0	127.0	9.0	11.0	180.0	183	bus

data.shape

(846, 19)

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 846 entries, 0 to 845
Data columns (total 19 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   compactness                  846 non-null    int64  
 1   circularity                  841 non-null    float64
 2   distance_circularity         842 non-null    float64
 3   radius_ratio                 840 non-null    float64
 4   pr.axis_aspect_ratio         844 non-null    float64
 5   max.length_aspect_ratio      846 non-null    int64  
 6   scatter_ratio                845 non-null    float64
 7   elongatedness                845 non-null    float64
 8   pr.axis_rectangularity       843 non-null    float64
 9   max.length_rectangularity    846 non-null    int64  
 10  scaled_variance              843 non-null    float64
 11  scaled_variance.1            844 non-null    float64
 12  scaled_radius_of_gyration    844 non-null    float64
 13  scaled_radius_of_gyration.1  842 non-null    float64
 14  skewness_about               840 non-null    float64
 15  skewness_about.1             845 non-null    float64
 16  skewness_about.2             845 non-null    float64
 17  hollows_ratio                846 non-null    int64  
 18  class                        846 non-null    object 
dtypes: float64(14), int64(4), object(1)
memory usage: 125.7+ KB

data.describe()

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio
count	846.000000	841.000000	842.000000	840.000000	844.000000	846.000000	845.000000	845.000000	843.000000	846.000000	843.000000	844.000000	844.000000	842.000000	840.000000	845.000000	845.000000	846.000000
mean	93.678487	44.828775	82.110451	168.888095	61.678910	8.567376	168.901775	40.933728	20.582444	147.998818	188.631079	439.494076	174.709716	72.447743	6.364286	12.602367	188.919527	195.632388
std	8.234474	6.152172	15.778292	33.520198	7.891463	4.601217	33.214848	7.816186	2.592933	14.515652	31.411004	176.666903	32.584808	7.486190	4.920649	8.936081	6.155809	7.438797
min	73.000000	33.000000	40.000000	104.000000	47.000000	2.000000	112.000000	26.000000	17.000000	118.000000	130.000000	184.000000	109.000000	59.000000	0.000000	0.000000	176.000000	181.000000
25%	87.000000	40.000000	70.000000	141.000000	57.000000	7.000000	147.000000	33.000000	19.000000	137.000000	167.000000	318.000000	149.000000	67.000000	2.000000	5.000000	184.000000	190.250000
50%	93.000000	44.000000	80.000000	167.000000	61.000000	8.000000	157.000000	43.000000	20.000000	146.000000	179.000000	363.500000	173.500000	71.500000	6.000000	11.000000	188.000000	197.000000
75%	100.000000	49.000000	98.000000	195.000000	65.000000	10.000000	198.000000	46.000000	23.000000	159.000000	217.000000	587.000000	198.000000	75.000000	9.000000	19.000000	193.000000	201.000000
max	119.000000	59.000000	112.000000	333.000000	138.000000	55.000000	265.000000	61.000000	29.000000	188.000000	320.000000	1018.000000	268.000000	135.000000	22.000000	41.000000	206.000000	211.000000

NaN Value Check

Missing values are present in the data - columns shown below:

1 circularity 841 non-null float64 2 distance_circularity 842 non-null float64 3 radius_ratio 840 non-null float64 4 pr.axis_aspect_ratio 844 non-null float64 6 scatter_ratio 845 non-null float64 7 elongatedness 845 non-null float64 8 pr.axis_rectangularity 843 non-null float64 10 scaled_variance 843 non-null float64 11 scaled_variance.1 844 non-null float64 12 scaled_radius_of_gyration 844 non-null float64 13 scaled_radius_of_gyration.1 842 non-null float64 14 skewness_about 840 non-null float64 15 skewness_about.1 845 non-null float64 16 skewness_about.2 845 non-null float64

nc_df=data.isna().apply(pd.value_counts)   #null value check
nc_df

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class
False	846.0	841	842	840	844	846.0	845	845	843	846.0	843	844	844	842	840	845	845	846.0	846.0
True	NaN	5	4	6	2	NaN	1	1	3	NaN	3	2	2	4	6	1	1	NaN	NaN

Checking for outliers

for col in data.columns:
    if data[col].dtype != object:
        sns.boxplot(x=data[col])
        plt.show()

cols_with_outliers=[]
for value in data.columns:
    if data[value].dtype != object:
        q1 = data[value].quantile(0.25) #first quartile value
        q3 = data[value].quantile(0.75) # third quartile value
        iqr = q3-q1 #Interquartile range
        low  = q1-2*iqr #acceptable range
        high = q3+2*iqr #acceptable range
        data_exclude = data.loc[(data[value] < low) | (data[value] > high)] #not meeting the acceptable range
        
        #tracking columns with outliers
        if len(data_exclude)>0:
            cols_with_outliers.append(value)

#columns with large outliers present taking threshold as 2*IQR
cols_with_outliers

['radius_ratio',
 'pr.axis_aspect_ratio',
 'max.length_aspect_ratio',
 'scaled_variance',
 'scaled_radius_of_gyration.1']

NaN value treatment

#columns with null values
nc_df_t=nc_df.iloc[1:].T
nc_df_t_c=nc_df_t[nc_df_t[1]>=1]
nc_df_t_c.reset_index()

	index	True
0	circularity	5.0
1	distance_circularity	4.0
2	radius_ratio	6.0
3	pr.axis_aspect_ratio	2.0
4	scatter_ratio	1.0
5	elongatedness	1.0
6	pr.axis_rectangularity	3.0
7	scaled_variance	3.0
8	scaled_variance.1	2.0
9	scaled_radius_of_gyration	2.0
10	scaled_radius_of_gyration.1	4.0
11	skewness_about	6.0
12	skewness_about.1	1.0
13	skewness_about.2	1.0

cols_with_nan=list(nc_df_t_c.index)
cols_with_nan

['circularity',
 'distance_circularity',
 'radius_ratio',
 'pr.axis_aspect_ratio',
 'scatter_ratio',
 'elongatedness',
 'pr.axis_rectangularity',
 'scaled_variance',
 'scaled_variance.1',
 'scaled_radius_of_gyration',
 'scaled_radius_of_gyration.1',
 'skewness_about',
 'skewness_about.1',
 'skewness_about.2']

For Columns with no outliers containing null values

#cols_with_nan-cols_with_outliers

no_cn=[x for x in cols_with_nan if x not in cols_with_outliers]
no_cn

['circularity',
 'distance_circularity',
 'scatter_ratio',
 'elongatedness',
 'pr.axis_rectangularity',
 'scaled_variance.1',
 'scaled_radius_of_gyration',
 'skewness_about',
 'skewness_about.1',
 'skewness_about.2']

#Replacing with mean values
for i in no_cn:
    mean_val=data[i].mean()
    data[i].fillna(mean_val,inplace=True)

data[data['circularity'].isnull()==True]

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class

For Columns with outliers containing null values

#cols_with_outliers union cols_with_nan

no_un=[x for x in cols_with_outliers if x in cols_with_nan]
no_un

['radius_ratio',
 'pr.axis_aspect_ratio',
 'scaled_variance',
 'scaled_radius_of_gyration.1']

#Replacing with median values
for i in no_un:
    median_val=data[i].median()
    data[i].fillna(median_val,inplace=True)

Finding relationship between independent & dependent variables

data['class'].value_counts()

car    429
bus    218
van    199
Name: class, dtype: int64

#encoding class variable
for i,val in enumerate(data['class']):
    if val=='car':
        data['class'][i]=1
    if val=='bus':
        data['class'][i]=2
    if val=='van':
        data['class'][i]=3
    
        

<ipython-input-182-440b1e2e3743>:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['class'][i]=3
<ipython-input-182-440b1e2e3743>:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['class'][i]=1
<ipython-input-182-440b1e2e3743>:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['class'][i]=2

data['class']=data['class'].astype(int)

data.hist(stacked=False, bins=100, figsize=(15,40), layout=(14,2));

sns.pairplot(data,diag_kind='kde')  #pairplot
plt.show()

plt.figure(figsize = (16,10))
sns.heatmap(data.corr(), annot=True)

<AxesSubplot:>

elongatedness and distance_circularity | distance_circularity and scatter_ratio | scatter_ratio and elongatedness | scatter_ratio and pr.axis_rectangularity | scaled_variance and elongatedness | circularity and max.length_rectangularity | scaled_variance.1 and scaled_variance |

shows very high correlations

Removing some of the very high correlated columns- elongatedness,pr.axis_rectangularity,scaled_variance.1 can be dropped

data.corr()["class"].sort_values(ascending= False)#correlation with target variable

class                          1.000000
elongatedness                  0.483326
scaled_radius_of_gyration.1    0.221288
pr.axis_aspect_ratio           0.038686
max.length_aspect_ratio        0.036942
skewness_about.2              -0.054890
skewness_about                -0.092923
max.length_rectangularity     -0.140152
hollows_ratio                 -0.142852
scaled_radius_of_gyration     -0.247706
circularity                   -0.251225
skewness_about.1              -0.271659
compactness                   -0.297774
radius_ratio                  -0.402095
scaled_variance               -0.407685
distance_circularity          -0.428136
scaled_variance.1             -0.444230
pr.axis_rectangularity        -0.444361
scatter_ratio                 -0.463979
Name: class, dtype: float64

pr.axis_aspect_ratio 0.038650 | max.length_aspect_ratio 0.036942 | skewness_about.2 -0.054890 |

shows very less correlation with the independend variable

Model

data.head()
y=data['class']
x=data.drop(['class','elongatedness','pr.axis_rectangularity','scaled_variance.1'], axis=1)

from sklearn.preprocessing import StandardScaler
std = StandardScaler()

# Scale data
X = std.fit_transform(x)

from sklearn.model_selection import train_test_split
# Split it to a 70:30 Ratio Train:Test

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=123)

X_train.shape

(592, 15)

len(y_train)

592

from sklearn import svm
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

clf = svm.SVC(gamma=0.025, C=8)   
clf.fit(X_train , y_train)
#y_pred = clf.predict(X_test)

SVC(C=8, gamma=0.025)

predictions = clf.predict(X_test)
score = clf.score(X_test, y_test)

print("Accuracy = " + str(score))
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))

Accuracy = 0.9566929133858267
[[123   1   2]
 [  0  64   3]
 [  3   2  56]]
              precision    recall  f1-score   support

           1       0.98      0.98      0.98       126
           2       0.96      0.96      0.96        67
           3       0.92      0.92      0.92        61

    accuracy                           0.96       254
   macro avg       0.95      0.95      0.95       254
weighted avg       0.96      0.96      0.96       254

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

num_folds = 20
seed = 7

kfold = KFold(n_splits=num_folds, random_state=seed)
results = cross_val_score(clf, X, y, cv=kfold)
print(results)
print("Accuracy: %.3f%% (%.3f%%)" % (results.mean()*100.0, results.std()*100.0))

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(

[0.93023256 0.97674419 0.95348837 1.         0.97674419 0.95348837
 1.         1.         1.         1.         0.97619048 0.97619048
 0.97619048 1.         0.95238095 1.         0.95238095 0.95238095
 0.97619048 1.        ]
Accuracy: 97.763% (2.161%)

from sklearn.decomposition import PCA
pca = PCA(n_components=10)
pca.fit(X)

PCA(n_components=10)

print(pca.explained_variance_ratio_)

[0.44603759 0.19916044 0.12199718 0.07584659 0.06060915 0.03317729
 0.0233393  0.01388082 0.01020675 0.00549394]

plt.step(list(range(1,11)),np.cumsum(pca.explained_variance_ratio_), where='mid')
plt.ylabel('Cum of variation explained')
plt.xlabel('eigen Value')
plt.show()

PCA with value 8 is what that capture about 95% of the variance in the data

pca3 = PCA(n_components=8)
pca3.fit(X)

Xpca3 = pca3.transform(X)

from sklearn.model_selection import train_test_split
# Split it to a 70:30 Ratio Train:Test

X_train, X_test, y_train, y_test = train_test_split(Xpca3, y, test_size=0.3,random_state=123)

clf2 = svm.SVC(gamma=0.025, C=8)   
clf2.fit(X_train , y_train)
#y_pred = clf.predict(X_test)

SVC(C=8, gamma=0.025)

predictions = clf2.predict(X_test)
score = clf2.score(X_test, y_test)

print("Accuracy = " + str(score))
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))

Accuracy = 0.9448818897637795
[[121   2   3]
 [  0  65   2]
 [  4   3  54]]
              precision    recall  f1-score   support

           1       0.97      0.96      0.96       126
           2       0.93      0.97      0.95        67
           3       0.92      0.89      0.90        61

    accuracy                           0.94       254
   macro avg       0.94      0.94      0.94       254
weighted avg       0.94      0.94      0.94       254

num_folds = 20
seed = 7

kfold = KFold(n_splits=num_folds, random_state=seed)
results = cross_val_score(clf2, Xpca3, y, cv=kfold)
print(results)
print("Accuracy: %.3f%% (%.3f%%)" % (results.mean()*100.0, results.std()*100.0))

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(

[0.93023256 0.95348837 0.95348837 1.         0.95348837 0.95348837
 0.97619048 0.97619048 0.92857143 0.97619048 0.95238095 0.97619048
 0.85714286 1.         0.9047619  0.92857143 0.95238095 0.95238095
 0.95238095 0.97619048]
Accuracy: 95.269% (3.189%)

accuracy scores and cross validation scores of Support vector machines – one trained using raw data

Accuracy = 0.9566929133858267 [[123 1 2] [ 0 64 3] [ 3 2 56]] precision recall f1-score support

       1       0.98      0.98      0.98       126
       2       0.96      0.96      0.96        67
       3       0.92      0.92      0.92        61

accuracy                           0.96       254

macro avg 0.95 0.95 0.95 254 weighted avg 0.96 0.96 0.96 254

Cross-validation score Accuracy: 97.763% (2.161%)

accuracy scores and cross validation scores of Support vector machines – one trained using Principal Components

Accuracy = 0.9448818897637795 [[121 2 3] [ 0 65 2] [ 4 3 54]] precision recall f1-score support

       1       0.97      0.96      0.96       126
       2       0.93      0.97      0.95        67
       3       0.92      0.89      0.90        61

accuracy                           0.94       254

macro avg 0.94 0.94 0.94 254 weighted avg 0.94 0.94 0.94 254

Cross-validation score Accuracy: 95.269% (3.189%)

Both the model performed comparitively same